US9069844B2 - Facilitating extraction and discovery of enterprise services - Google Patents
Facilitating extraction and discovery of enterprise services Download PDFInfo
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- US9069844B2 US9069844B2 US13/287,634 US201113287634A US9069844B2 US 9069844 B2 US9069844 B2 US 9069844B2 US 201113287634 A US201113287634 A US 201113287634A US 9069844 B2 US9069844 B2 US 9069844B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3329—Natural language query formulation or dialogue systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
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- G06F17/30654—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/3332—Query translation
- G06F16/3338—Query expansion
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- G06F17/30672—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
Abstract
Description
where each non-zero diagonal value is a probability associated with a concept Ci displayed above the corresponding column of the matrix MCL. A concept matrix MC is generated (410). In some examples, after the MCA and MCL are populated, the suggestions module can generate the concept matrix based on MCA and MCL by, for example, multiplying MCA and MCL. For the current example, MC can be provided as:
Potential concepts are ranked (412). In some examples, the automaton handler can rank the concepts Ci based on their accumulated sums of occurrence probabilities. Continuing with the example above, column C4 has an accumulated sum of occurrence probabilities equal to 0.2+0.2+0.2=0.6, while columns C1, C2, and C5 have sums equal to 0.5, 0.4, and 0.2, respectively. Outgoing concepts COUT are provided (414). Applying an example accumulated sum of occurrence threshold (e.g., ≧0.5), concepts C4 and C1 are selected and are provided as outgoing concepts COUT={C4, C1}, because their respective sums are each greater than or equal to the occurrence threshold.
As an example, a search term ‘Sales’ can be associated with a similar, potential fact ‘Sales Order’ based on a high similarity value. In contrast, the fact ‘Sales Price Specification Calculation’ would receive a relatively low similarity value. Using these examples, a similarity vector can include:
In the case where outgoing concepts are received by the knowledge base handler, facts associated with the outgoing concepts are identified (508) and a document frequency vector VFD is generated (510). In some examples, the knowledge base handler can identifies query facts F based on COUT and generates the document frequency vector. An example document frequency vector can be provided as:
As an example, for the outgoing concept “Business Object,” obtain all facts associated to the concept are identified (e.g. “Sales Order”, “Material” etc.). The document frequency vector then describes how frequent these facts appear among all annotations. In this case, the annotation “Sales Order” appeared more often than “Material” as shown in VFD:
Suggested facts are provided based on the similarity vector and the document frequency vector (512). In some implementations, the knowledge base handler can provide a list of suggested facts Fs={Fi, . . . , Fq} based on VFK and VFD by, for example, intersecting VFK and VFD and multiplying their probabilities:
In this example, Fs={‘SalesOrder’, ‘SalesOrderX’}. In the case where outgoing concepts are not received by the knowledge base handler (i.e., the pre-processing module does not provide incoming concepts to the automaton handler, the list of suggested facts includes facts identified associated with the similarity vector VFK. The list of facts are provided to the querying module, which builds one or more queries based on the facts and generates search results based on the queries.
Claims (18)
Priority Applications (3)
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US13/287,634 US9069844B2 (en) | 2011-11-02 | 2011-11-02 | Facilitating extraction and discovery of enterprise services |
EP20120007449 EP2592572A1 (en) | 2011-11-02 | 2012-10-31 | Facilitating extraction and discovery of enterprise services |
US14/718,376 US9740754B2 (en) | 2011-11-02 | 2015-05-21 | Facilitating extraction and discovery of enterprise services |
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US13/287,634 US9069844B2 (en) | 2011-11-02 | 2011-11-02 | Facilitating extraction and discovery of enterprise services |
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US14/718,376 Continuation US9740754B2 (en) | 2011-11-02 | 2015-05-21 | Facilitating extraction and discovery of enterprise services |
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US20130110861A1 US20130110861A1 (en) | 2013-05-02 |
US9069844B2 true US9069844B2 (en) | 2015-06-30 |
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US13/287,634 Active 2033-12-03 US9069844B2 (en) | 2011-11-02 | 2011-11-02 | Facilitating extraction and discovery of enterprise services |
US14/718,376 Active 2032-09-02 US9740754B2 (en) | 2011-11-02 | 2015-05-21 | Facilitating extraction and discovery of enterprise services |
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EP (1) | EP2592572A1 (en) |
Cited By (1)
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US10313410B2 (en) | 2014-03-21 | 2019-06-04 | Ptc Inc. | Systems and methods using binary dynamic rest messages |
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US11074266B2 (en) * | 2018-10-11 | 2021-07-27 | International Business Machines Corporation | Semantic concept discovery over event databases |
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